68.4 kB

	"""
	Classes to support contour plotting and labelling for the Axes class.
	"""

	from contextlib import ExitStack
	import functools
	import math
	from numbers import Integral

	import numpy as np
	from numpy import ma

	import matplotlib as mpl
	from matplotlib import _api, _docstring
	from matplotlib.backend_bases import MouseButton
	from matplotlib.lines import Line2D
	from matplotlib.path import Path
	from matplotlib.text import Text
	import matplotlib.ticker as ticker
	import matplotlib.cm as cm
	import matplotlib.colors as mcolors
	import matplotlib.collections as mcoll
	import matplotlib.font_manager as font_manager
	import matplotlib.cbook as cbook
	import matplotlib.patches as mpatches
	import matplotlib.transforms as mtransforms


	def _contour_labeler_event_handler(cs, inline, inline_spacing, event):
	canvas = cs.axes.get_figure(root=True).canvas
	is_button = event.name == "button_press_event"
	is_key = event.name == "key_press_event"
	# Quit (even if not in infinite mode; this is consistent with
	# MATLAB and sometimes quite useful, but will require the user to
	# test how many points were actually returned before using data).
	if (is_button and event.button == MouseButton.MIDDLE
	or is_key and event.key in ["escape", "enter"]):
	canvas.stop_event_loop()
	# Pop last click.
	elif (is_button and event.button == MouseButton.RIGHT
	or is_key and event.key in ["backspace", "delete"]):
	# Unfortunately, if one is doing inline labels, then there is currently
	# no way to fix the broken contour - once humpty-dumpty is broken, he
	# can't be put back together. In inline mode, this does nothing.
	if not inline:
	cs.pop_label()
	canvas.draw()
	# Add new click.
	elif (is_button and event.button == MouseButton.LEFT
	# On macOS/gtk, some keys return None.
	or is_key and event.key is not None):
	if cs.axes.contains(event)[0]:
	cs.add_label_near(event.x, event.y, transform=False,
	inline=inline, inline_spacing=inline_spacing)
	canvas.draw()


	class ContourLabeler:
	"""Mixin to provide labelling capability to `.ContourSet`."""

	def clabel(self, levels=None, *,
	fontsize=None, inline=True, inline_spacing=5, fmt=None,
	colors=None, use_clabeltext=False, manual=False,
	rightside_up=True, zorder=None):
	"""
	Label a contour plot.

	Adds labels to line contours in this `.ContourSet` (which inherits from
	this mixin class).

	Parameters
	----------
	levels : array-like, optional
	A list of level values, that should be labeled. The list must be
	a subset of ``cs.levels``. If not given, all levels are labeled.

	fontsize : str or float, default: :rc:`font.size`
	Size in points or relative size e.g., 'smaller', 'x-large'.
	See `.Text.set_size` for accepted string values.

	colors : :mpltype:`color` or colors or None, default: None
	The label colors:

	- If None, the color of each label matches the color of
	the corresponding contour.

	- If one string color, e.g., colors = 'r' or colors =
	'red', all labels will be plotted in this color.

	- If a tuple of colors (string, float, RGB, etc), different labels
	will be plotted in different colors in the order specified.

	inline : bool, default: True
	If ``True`` the underlying contour is removed where the label is
	placed.

	inline_spacing : float, default: 5
	Space in pixels to leave on each side of label when placing inline.

	This spacing will be exact for labels at locations where the
	contour is straight, less so for labels on curved contours.

	fmt : `.Formatter` or str or callable or dict, optional
	How the levels are formatted:

	- If a `.Formatter`, it is used to format all levels at once, using
	its `.Formatter.format_ticks` method.
	- If a str, it is interpreted as a %-style format string.
	- If a callable, it is called with one level at a time and should
	return the corresponding label.
	- If a dict, it should directly map levels to labels.

	The default is to use a standard `.ScalarFormatter`.

	manual : bool or iterable, default: False
	If ``True``, contour labels will be placed manually using
	mouse clicks. Click the first button near a contour to
	add a label, click the second button (or potentially both
	mouse buttons at once) to finish adding labels. The third
	button can be used to remove the last label added, but
	only if labels are not inline. Alternatively, the keyboard
	can be used to select label locations (enter to end label
	placement, delete or backspace act like the third mouse button,
	and any other key will select a label location).

	manual can also be an iterable object of (x, y) tuples.
	Contour labels will be created as if mouse is clicked at each
	(x, y) position.

	rightside_up : bool, default: True
	If ``True``, label rotations will always be plus
	or minus 90 degrees from level.

	use_clabeltext : bool, default: False
	If ``True``, use `.Text.set_transform_rotates_text` to ensure that
	label rotation is updated whenever the Axes aspect changes.

	zorder : float or None, default: ``(2 + contour.get_zorder())``
	zorder of the contour labels.

	Returns
	-------
	labels
	A list of `.Text` instances for the labels.
	"""

	# Based on the input arguments, clabel() adds a list of "label
	# specific" attributes to the ContourSet object. These attributes are
	# all of the form label* and names should be fairly self explanatory.
	#
	# Once these attributes are set, clabel passes control to the labels()
	# method (for automatic label placement) or blocking_input_loop and
	# _contour_labeler_event_handler (for manual label placement).

	if fmt is None:
	fmt = ticker.ScalarFormatter(useOffset=False)
	fmt.create_dummy_axis()
	self.labelFmt = fmt
	self._use_clabeltext = use_clabeltext
	self.labelManual = manual
	self.rightside_up = rightside_up
	self._clabel_zorder = 2 + self.get_zorder() if zorder is None else zorder

	if levels is None:
	levels = self.levels
	indices = list(range(len(self.cvalues)))
	else:
	levlabs = list(levels)
	indices, levels = [], []
	for i, lev in enumerate(self.levels):
	if lev in levlabs:
	indices.append(i)
	levels.append(lev)
	if len(levels) < len(levlabs):
	raise ValueError(f"Specified levels {levlabs} don't match "
	f"available levels {self.levels}")
	self.labelLevelList = levels
	self.labelIndiceList = indices

	self._label_font_props = font_manager.FontProperties(size=fontsize)

	if colors is None:
	self.labelMappable = self
	self.labelCValueList = np.take(self.cvalues, self.labelIndiceList)
	else:
	cmap = mcolors.ListedColormap(colors, N=len(self.labelLevelList))
	self.labelCValueList = list(range(len(self.labelLevelList)))
	self.labelMappable = cm.ScalarMappable(cmap=cmap,
	norm=mcolors.NoNorm())

	self.labelXYs = []

	if np.iterable(manual):
	for x, y in manual:
	self.add_label_near(x, y, inline, inline_spacing)
	elif manual:
	print('Select label locations manually using first mouse button.')
	print('End manual selection with second mouse button.')
	if not inline:
	print('Remove last label by clicking third mouse button.')
	mpl._blocking_input.blocking_input_loop(
	self.axes.get_figure(root=True),
	["button_press_event", "key_press_event"],
	timeout=-1, handler=functools.partial(
	_contour_labeler_event_handler,
	self, inline, inline_spacing))
	else:
	self.labels(inline, inline_spacing)

	return cbook.silent_list('text.Text', self.labelTexts)

	def print_label(self, linecontour, labelwidth):
	"""Return whether a contour is long enough to hold a label."""
	return (len(linecontour) > 10 * labelwidth
	or (len(linecontour)
	and (np.ptp(linecontour, axis=0) > 1.2 * labelwidth).any()))

	def too_close(self, x, y, lw):
	"""Return whether a label is already near this location."""
	thresh = (1.2 * lw) ** 2
	return any((x - loc[0]) 2 + (y - loc[1]) 2 < thresh
	for loc in self.labelXYs)

	def _get_nth_label_width(self, nth):
	"""Return the width of the nth label, in pixels."""
	fig = self.axes.get_figure(root=False)
	renderer = fig.get_figure(root=True)._get_renderer()
	return (Text(0, 0,
	self.get_text(self.labelLevelList[nth], self.labelFmt),
	figure=fig, fontproperties=self._label_font_props)
	.get_window_extent(renderer).width)

	def get_text(self, lev, fmt):
	"""Get the text of the label."""
	if isinstance(lev, str):
	return lev
	elif isinstance(fmt, dict):
	return fmt.get(lev, '%1.3f')
	elif callable(getattr(fmt, "format_ticks", None)):
	return fmt.format_ticks([*self.labelLevelList, lev])[-1]
	elif callable(fmt):
	return fmt(lev)
	else:
	return fmt % lev

	def locate_label(self, linecontour, labelwidth):
	"""
	Find good place to draw a label (relatively flat part of the contour).
	"""
	ctr_size = len(linecontour)
	n_blocks = int(np.ceil(ctr_size / labelwidth)) if labelwidth > 1 else 1
	block_size = ctr_size if n_blocks == 1 else int(labelwidth)
	# Split contour into blocks of length ``block_size``, filling the last
	# block by cycling the contour start (per `np.resize` semantics). (Due
	# to cycling, the index returned is taken modulo ctr_size.)
	xx = np.resize(linecontour[:, 0], (n_blocks, block_size))
	yy = np.resize(linecontour[:, 1], (n_blocks, block_size))
	yfirst = yy[:, :1]
	ylast = yy[:, -1:]
	xfirst = xx[:, :1]
	xlast = xx[:, -1:]
	s = (yfirst - yy) * (xlast - xfirst) - (xfirst - xx) * (ylast - yfirst)
	l = np.hypot(xlast - xfirst, ylast - yfirst)
	# Ignore warning that divide by zero throws, as this is a valid option
	with np.errstate(divide='ignore', invalid='ignore'):
	distances = (abs(s) / l).sum(axis=-1)
	# Labels are drawn in the middle of the block (``hbsize``) where the
	# contour is the closest (per ``distances``) to a straight line, but
	# not `too_close()` to a preexisting label.
	hbsize = block_size // 2
	adist = np.argsort(distances)
	# If all candidates are `too_close()`, go back to the straightest part
	# (``adist[0]``).
	for idx in np.append(adist, adist[0]):
	x, y = xx[idx, hbsize], yy[idx, hbsize]
	if not self.too_close(x, y, labelwidth):
	break
	return x, y, (idx * block_size + hbsize) % ctr_size

	def _split_path_and_get_label_rotation(self, path, idx, screen_pos, lw, spacing=5):
	"""
	Prepare for insertion of a label at index idx of path.

	Parameters
	----------
	path : Path
	The path where the label will be inserted, in data space.
	idx : int
	The vertex index after which the label will be inserted.
	screen_pos : (float, float)
	The position where the label will be inserted, in screen space.
	lw : float
	The label width, in screen space.
	spacing : float
	Extra spacing around the label, in screen space.

	Returns
	-------
	path : Path
	The path, broken so that the label can be drawn over it.
	angle : float
	The rotation of the label.

	Notes
	-----
	Both tasks are done together to avoid calculating path lengths multiple times,
	which is relatively costly.

	The method used here involves computing the path length along the contour in
	pixel coordinates and then looking (label width / 2) away from central point to
	determine rotation and then to break contour if desired. The extra spacing is
	taken into account when breaking the path, but not when computing the angle.
	"""
	xys = path.vertices
	codes = path.codes

	# Insert a vertex at idx/pos (converting back to data space), if there isn't yet
	# a vertex there. With infinite precision one could also always insert the
	# extra vertex (it will get masked out by the label below anyways), but floating
	# point inaccuracies (the point can have undergone a data->screen->data
	# transform loop) can slightly shift the point and e.g. shift the angle computed
	# below from exactly zero to nonzero.
	pos = self.get_transform().inverted().transform(screen_pos)
	if not np.allclose(pos, xys[idx]):
	xys = np.insert(xys, idx, pos, axis=0)
	codes = np.insert(codes, idx, Path.LINETO)

	# Find the connected component where the label will be inserted. Note that a
	# path always starts with a MOVETO, and we consider there's an implicit
	# MOVETO (closing the last path) at the end.
	movetos = (codes == Path.MOVETO).nonzero()[0]
	start = movetos[movetos <= idx][-1]
	try:
	stop = movetos[movetos > idx][0]
	except IndexError:
	stop = len(codes)

	# Restrict ourselves to the connected component.
	cc_xys = xys[start:stop]
	idx -= start

	# If the path is closed, rotate it s.t. it starts at the label.
	is_closed_path = codes[stop - 1] == Path.CLOSEPOLY
	if is_closed_path:
	cc_xys = np.concatenate([cc_xys[idx:-1], cc_xys[:idx+1]])
	idx = 0

	# Like np.interp, but additionally vectorized over fp.
	def interp_vec(x, xp, fp): return [np.interp(x, xp, col) for col in fp.T]

	# Use cumulative path lengths ("cpl") as curvilinear coordinate along contour.
	screen_xys = self.get_transform().transform(cc_xys)
	path_cpls = np.insert(
	np.cumsum(np.hypot(*np.diff(screen_xys, axis=0).T)), 0, 0)
	path_cpls -= path_cpls[idx]

	# Use linear interpolation to get end coordinates of label.
	target_cpls = np.array([-lw/2, lw/2])
	if is_closed_path: # For closed paths, target from the other end.
	target_cpls[0] += (path_cpls[-1] - path_cpls[0])
	(sx0, sx1), (sy0, sy1) = interp_vec(target_cpls, path_cpls, screen_xys)
	angle = np.rad2deg(np.arctan2(sy1 - sy0, sx1 - sx0)) # Screen space.
	if self.rightside_up: # Fix angle so text is never upside-down
	angle = (angle + 90) % 180 - 90

	target_cpls += [-spacing, +spacing] # Expand range by spacing.

	# Get indices near points of interest; use -1 as out of bounds marker.
	i0, i1 = np.interp(target_cpls, path_cpls, range(len(path_cpls)),
	left=-1, right=-1)
	i0 = math.floor(i0)
	i1 = math.ceil(i1)
	(x0, x1), (y0, y1) = interp_vec(target_cpls, path_cpls, cc_xys)

	# Actually break contours (dropping zero-len parts).
	new_xy_blocks = []
	new_code_blocks = []
	if is_closed_path:
	if i0 != -1 and i1 != -1:
	# This is probably wrong in the case that the entire contour would
	# be discarded, but ensures that a valid path is returned and is
	# consistent with behavior of mpl <3.8
	points = cc_xys[i1:i0+1]
	new_xy_blocks.extend([[(x1, y1)], points, [(x0, y0)]])
	nlines = len(points) + 1
	new_code_blocks.extend([[Path.MOVETO], [Path.LINETO] * nlines])
	else:
	if i0 != -1:
	new_xy_blocks.extend([cc_xys[:i0 + 1], [(x0, y0)]])
	new_code_blocks.extend([[Path.MOVETO], [Path.LINETO] * (i0 + 1)])
	if i1 != -1:
	new_xy_blocks.extend([[(x1, y1)], cc_xys[i1:]])
	new_code_blocks.extend([
	[Path.MOVETO], [Path.LINETO] * (len(cc_xys) - i1)])

	# Back to the full path.
	xys = np.concatenate([xys[:start], *new_xy_blocks, xys[stop:]])
	codes = np.concatenate([codes[:start], *new_code_blocks, codes[stop:]])

	return angle, Path(xys, codes)

	def add_label(self, x, y, rotation, lev, cvalue):
	"""Add a contour label, respecting whether use_clabeltext was set."""
	data_x, data_y = self.axes.transData.inverted().transform((x, y))
	t = Text(
	data_x, data_y,
	text=self.get_text(lev, self.labelFmt),
	rotation=rotation,
	horizontalalignment='center', verticalalignment='center',
	zorder=self._clabel_zorder,
	color=self.labelMappable.to_rgba(cvalue, alpha=self.get_alpha()),
	fontproperties=self._label_font_props,
	clip_box=self.axes.bbox)
	if self._use_clabeltext:
	data_rotation, = self.axes.transData.inverted().transform_angles(
	[rotation], [[x, y]])
	t.set(rotation=data_rotation, transform_rotates_text=True)
	self.labelTexts.append(t)
	self.labelCValues.append(cvalue)
	self.labelXYs.append((x, y))
	# Add label to plot here - useful for manual mode label selection
	self.axes.add_artist(t)

	def add_label_near(self, x, y, inline=True, inline_spacing=5,
	transform=None):
	"""
	Add a label near the point ``(x, y)``.

	Parameters
	----------
	x, y : float
	The approximate location of the label.
	inline : bool, default: True
	If True remove the segment of the contour beneath the label.
	inline_spacing : int, default: 5
	Space in pixels to leave on each side of label when placing
	inline. This spacing will be exact for labels at locations where
	the contour is straight, less so for labels on curved contours.
	transform : `.Transform` or `False`, default: ``self.axes.transData``
	A transform applied to ``(x, y)`` before labeling. The default
	causes ``(x, y)`` to be interpreted as data coordinates. `False`
	is a synonym for `.IdentityTransform`; i.e. ``(x, y)`` should be
	interpreted as display coordinates.
	"""

	if transform is None:
	transform = self.axes.transData
	if transform:
	x, y = transform.transform((x, y))

	idx_level_min, idx_vtx_min, proj = self._find_nearest_contour(
	(x, y), self.labelIndiceList)
	path = self._paths[idx_level_min]
	level = self.labelIndiceList.index(idx_level_min)
	label_width = self._get_nth_label_width(level)
	rotation, path = self._split_path_and_get_label_rotation(
	path, idx_vtx_min, proj, label_width, inline_spacing)
	self.add_label(*proj, rotation, self.labelLevelList[idx_level_min],
	self.labelCValueList[idx_level_min])

	if inline:
	self._paths[idx_level_min] = path

	def pop_label(self, index=-1):
	"""Defaults to removing last label, but any index can be supplied"""
	self.labelCValues.pop(index)
	t = self.labelTexts.pop(index)
	t.remove()

	def labels(self, inline, inline_spacing):
	for idx, (icon, lev, cvalue) in enumerate(zip(
	self.labelIndiceList,
	self.labelLevelList,
	self.labelCValueList,
	)):
	trans = self.get_transform()
	label_width = self._get_nth_label_width(idx)
	additions = []
	for subpath in self._paths[icon]._iter_connected_components():
	screen_xys = trans.transform(subpath.vertices)
	# Check if long enough for a label
	if self.print_label(screen_xys, label_width):
	x, y, idx = self.locate_label(screen_xys, label_width)
	rotation, path = self._split_path_and_get_label_rotation(
	subpath, idx, (x, y),
	label_width, inline_spacing)
	self.add_label(x, y, rotation, lev, cvalue) # Really add label.
	if inline: # If inline, add new contours
	additions.append(path)
	else: # If not adding label, keep old path
	additions.append(subpath)
	# After looping over all segments on a contour, replace old path by new one
	# if inlining.
	if inline:
	self._paths[icon] = Path.make_compound_path(*additions)

	def remove(self):
	super().remove()
	for text in self.labelTexts:
	text.remove()


	def _find_closest_point_on_path(xys, p):
	"""
	Parameters
	----------
	xys : (N, 2) array-like
	Coordinates of vertices.
	p : (float, float)
	Coordinates of point.

	Returns
	-------
	d2min : float
	Minimum square distance of p to xys.
	proj : (float, float)
	Projection of p onto xys.
	imin : (int, int)
	Consecutive indices of vertices of segment in xys where proj is.
	Segments are considered as including their end-points; i.e. if the
	closest point on the path is a node in xys with index i, this
	returns ``(i-1, i)``. For the special case where xys is a single
	point, this returns ``(0, 0)``.
	"""
	if len(xys) == 1:
	return (((p - xys[0]) ** 2).sum(), xys[0], (0, 0))
	dxys = xys[1:] - xys[:-1] # Individual segment vectors.
	norms = (dxys ** 2).sum(axis=1)
	norms[norms == 0] = 1 # For zero-length segment, replace 0/0 by 0/1.
	rel_projs = np.clip( # Project onto each segment in relative 0-1 coords.
	((p - xys[:-1]) * dxys).sum(axis=1) / norms,
	0, 1)[:, None]
	projs = xys[:-1] + rel_projs * dxys # Projs. onto each segment, in (x, y).
	d2s = ((projs - p) ** 2).sum(axis=1) # Squared distances.
	imin = np.argmin(d2s)
	return (d2s[imin], projs[imin], (imin, imin+1))


	_docstring.interpd.register(contour_set_attributes=r"""
	Attributes
	----------
	levels : array
	The values of the contour levels.

	layers : array
	Same as levels for line contours; half-way between
	levels for filled contours. See ``ContourSet._process_colors``.
	""")


	@_docstring.interpd
	class ContourSet(ContourLabeler, mcoll.Collection):
	"""
	Store a set of contour lines or filled regions.

	User-callable method: `~.Axes.clabel`

	Parameters
	----------
	ax : `~matplotlib.axes.Axes`

	levels : [level0, level1, ..., leveln]
	A list of floating point numbers indicating the contour levels.

	allsegs : [level0segs, level1segs, ...]
	List of all the polygon segments for all the levels.
	For contour lines ``len(allsegs) == len(levels)``, and for
	filled contour regions ``len(allsegs) = len(levels)-1``. The lists
	should look like ::

	level0segs = [polygon0, polygon1, ...]
	polygon0 = [[x0, y0], [x1, y1], ...]

	allkinds : ``None`` or [level0kinds, level1kinds, ...]
	Optional list of all the polygon vertex kinds (code types), as
	described and used in Path. This is used to allow multiply-
	connected paths such as holes within filled polygons.
	If not ``None``, ``len(allkinds) == len(allsegs)``. The lists
	should look like ::

	level0kinds = [polygon0kinds, ...]
	polygon0kinds = [vertexcode0, vertexcode1, ...]

	If allkinds is not ``None``, usually all polygons for a
	particular contour level are grouped together so that
	``level0segs = [polygon0]`` and ``level0kinds = [polygon0kinds]``.

	**kwargs
	Keyword arguments are as described in the docstring of
	`~.Axes.contour`.

	%(contour_set_attributes)s
	"""

	def __init__(self, ax, *args,
	levels=None, filled=False, linewidths=None, linestyles=None,
	hatches=(None,), alpha=None, origin=None, extent=None,
	cmap=None, colors=None, norm=None, vmin=None, vmax=None,
	colorizer=None, extend='neither', antialiased=None, nchunk=0,
	locator=None, transform=None, negative_linestyles=None, clip_path=None,
	**kwargs):
	"""
	Draw contour lines or filled regions, depending on
	whether keyword arg filled is ``False`` (default) or ``True``.

	Call signature::

	ContourSet(ax, levels, allsegs, [allkinds], **kwargs)

	Parameters
	----------
	ax : `~matplotlib.axes.Axes`
	The `~.axes.Axes` object to draw on.

	levels : [level0, level1, ..., leveln]
	A list of floating point numbers indicating the contour
	levels.

	allsegs : [level0segs, level1segs, ...]
	List of all the polygon segments for all the levels.
	For contour lines ``len(allsegs) == len(levels)``, and for
	filled contour regions ``len(allsegs) = len(levels)-1``. The lists
	should look like ::

	level0segs = [polygon0, polygon1, ...]
	polygon0 = [[x0, y0], [x1, y1], ...]

	allkinds : [level0kinds, level1kinds, ...], optional
	Optional list of all the polygon vertex kinds (code types), as
	described and used in Path. This is used to allow multiply-
	connected paths such as holes within filled polygons.
	If not ``None``, ``len(allkinds) == len(allsegs)``. The lists
	should look like ::

	level0kinds = [polygon0kinds, ...]
	polygon0kinds = [vertexcode0, vertexcode1, ...]

	If allkinds is not ``None``, usually all polygons for a
	particular contour level are grouped together so that
	``level0segs = [polygon0]`` and ``level0kinds = [polygon0kinds]``.

	**kwargs
	Keyword arguments are as described in the docstring of
	`~.Axes.contour`.
	"""
	if antialiased is None and filled:
	# Eliminate artifacts; we are not stroking the boundaries.
	antialiased = False
	# The default for line contours will be taken from the
	# LineCollection default, which uses :rc:`lines.antialiased`.
	super().__init__(
	antialiaseds=antialiased,
	alpha=alpha,
	clip_path=clip_path,
	transform=transform,
	colorizer=colorizer,
	)
	self.axes = ax
	self.levels = levels
	self.filled = filled
	self.hatches = hatches
	self.origin = origin
	self.extent = extent
	self.colors = colors
	self.extend = extend

	self.nchunk = nchunk
	self.locator = locator

	if colorizer:
	self._set_colorizer_check_keywords(colorizer, cmap=cmap,
	norm=norm, vmin=vmin,
	vmax=vmax, colors=colors)
	norm = colorizer.norm
	cmap = colorizer.cmap
	if (isinstance(norm, mcolors.LogNorm)
	or isinstance(self.locator, ticker.LogLocator)):
	self.logscale = True
	if norm is None:
	norm = mcolors.LogNorm()
	else:
	self.logscale = False

	_api.check_in_list([None, 'lower', 'upper', 'image'], origin=origin)
	if self.extent is not None and len(self.extent) != 4:
	raise ValueError(
	"If given, 'extent' must be None or (x0, x1, y0, y1)")
	if self.colors is not None and cmap is not None:
	raise ValueError('Either colors or cmap must be None')
	if self.origin == 'image':
	self.origin = mpl.rcParams['image.origin']

	self._orig_linestyles = linestyles # Only kept for user access.
	self.negative_linestyles = negative_linestyles
	# If negative_linestyles was not defined as a keyword argument, define
	# negative_linestyles with rcParams
	if self.negative_linestyles is None:
	self.negative_linestyles = \
	mpl.rcParams['contour.negative_linestyle']

	kwargs = self._process_args(args, *kwargs)
	self._process_levels()

	self._extend_min = self.extend in ['min', 'both']
	self._extend_max = self.extend in ['max', 'both']
	if self.colors is not None:
	if mcolors.is_color_like(self.colors):
	color_sequence = [self.colors]
	else:
	color_sequence = self.colors

	ncolors = len(self.levels)
	if self.filled:
	ncolors -= 1
	i0 = 0

	# Handle the case where colors are given for the extended
	# parts of the contour.

	use_set_under_over = False
	# if we are extending the lower end, and we've been given enough
	# colors then skip the first color in the resulting cmap. For the
	# extend_max case we don't need to worry about passing more colors
	# than ncolors as ListedColormap will clip.
	total_levels = (ncolors +
	int(self._extend_min) +
	int(self._extend_max))
	if (len(color_sequence) == total_levels and
	(self._extend_min or self._extend_max)):
	use_set_under_over = True
	if self._extend_min:
	i0 = 1

	cmap = mcolors.ListedColormap(color_sequence[i0:None], N=ncolors)

	if use_set_under_over:
	if self._extend_min:
	cmap.set_under(color_sequence[0])
	if self._extend_max:
	cmap.set_over(color_sequence[-1])

	# label lists must be initialized here
	self.labelTexts = []
	self.labelCValues = []

	self.set_cmap(cmap)
	if norm is not None:
	self.set_norm(norm)
	with self.norm.callbacks.blocked(signal="changed"):
	if vmin is not None:
	self.norm.vmin = vmin
	if vmax is not None:
	self.norm.vmax = vmax
	self.norm._changed()
	self._process_colors()

	if self._paths is None:
	self._paths = self._make_paths_from_contour_generator()

	if self.filled:
	if linewidths is not None:
	_api.warn_external('linewidths is ignored by contourf')
	# Lower and upper contour levels.
	lowers, uppers = self._get_lowers_and_uppers()
	self.set(
	edgecolor="none",
	# Default zorder taken from Collection
	zorder=kwargs.pop("zorder", 1),
	)

	else:
	self.set(
	facecolor="none",
	linewidths=self._process_linewidths(linewidths),
	linestyle=self._process_linestyles(linestyles),
	# Default zorder taken from LineCollection, which is higher
	# than for filled contours so that lines are displayed on top.
	zorder=kwargs.pop("zorder", 2),
	label="_nolegend_",
	)

	self.axes.add_collection(self, autolim=False)
	self.sticky_edges.x[:] = [self._mins[0], self._maxs[0]]
	self.sticky_edges.y[:] = [self._mins[1], self._maxs[1]]
	self.axes.update_datalim([self._mins, self._maxs])
	self.axes.autoscale_view(tight=True)

	self.changed() # set the colors

	if kwargs:
	_api.warn_external(
	'The following kwargs were not used by contour: ' +
	", ".join(map(repr, kwargs))
	)

	allsegs = property(lambda self: [
	[subp.vertices for subp in p._iter_connected_components()]
	for p in self.get_paths()])
	allkinds = property(lambda self: [
	[subp.codes for subp in p._iter_connected_components()]
	for p in self.get_paths()])
	alpha = property(lambda self: self.get_alpha())
	linestyles = property(lambda self: self._orig_linestyles)

	def get_transform(self):
	"""Return the `.Transform` instance used by this ContourSet."""
	if self._transform is None:
	self._transform = self.axes.transData
	elif (not isinstance(self._transform, mtransforms.Transform)
	and hasattr(self._transform, '_as_mpl_transform')):
	self._transform = self._transform._as_mpl_transform(self.axes)
	return self._transform

	def __getstate__(self):
	state = self.__dict__.copy()
	# the C object _contour_generator cannot currently be pickled. This
	# isn't a big issue as it is not actually used once the contour has
	# been calculated.
	state['_contour_generator'] = None
	return state

	def legend_elements(self, variable_name='x', str_format=str):
	"""
	Return a list of artists and labels suitable for passing through
	to `~.Axes.legend` which represent this ContourSet.

	The labels have the form "0 < x <= 1" stating the data ranges which
	the artists represent.

	Parameters
	----------
	variable_name : str
	The string used inside the inequality used on the labels.
	str_format : function: float -> str
	Function used to format the numbers in the labels.

	Returns
	-------
	artists : list[`.Artist`]
	A list of the artists.
	labels : list[str]
	A list of the labels.
	"""
	artists = []
	labels = []

	if self.filled:
	lowers, uppers = self._get_lowers_and_uppers()
	n_levels = len(self._paths)
	for idx in range(n_levels):
	artists.append(mpatches.Rectangle(
	(0, 0), 1, 1,
	facecolor=self.get_facecolor()[idx],
	hatch=self.hatches[idx % len(self.hatches)],
	))
	lower = str_format(lowers[idx])
	upper = str_format(uppers[idx])
	if idx == 0 and self.extend in ('min', 'both'):
	labels.append(fr'${variable_name} \leq {lower}s$')
	elif idx == n_levels - 1 and self.extend in ('max', 'both'):
	labels.append(fr'${variable_name} > {upper}s$')
	else:
	labels.append(fr'${lower} < {variable_name} \leq {upper}$')
	else:
	for idx, level in enumerate(self.levels):
	artists.append(Line2D(
	[], [],
	color=self.get_edgecolor()[idx],
	linewidth=self.get_linewidths()[idx],
	linestyle=self.get_linestyles()[idx],
	))
	labels.append(fr'${variable_name} = {str_format(level)}$')

	return artists, labels

	def _process_args(self, args, *kwargs):
	"""
	Process args and kwargs; override in derived classes.

	Must set self.levels, self.zmin and self.zmax, and update Axes limits.
	"""
	self.levels = args[0]
	allsegs = args[1]
	allkinds = args[2] if len(args) > 2 else None
	self.zmax = np.max(self.levels)
	self.zmin = np.min(self.levels)

	if allkinds is None:
	allkinds = [[None] * len(segs) for segs in allsegs]

	# Check lengths of levels and allsegs.
	if self.filled:
	if len(allsegs) != len(self.levels) - 1:
	raise ValueError('must be one less number of segments as '
	'levels')
	else:
	if len(allsegs) != len(self.levels):
	raise ValueError('must be same number of segments as levels')

	# Check length of allkinds.
	if len(allkinds) != len(allsegs):
	raise ValueError('allkinds has different length to allsegs')

	# Determine x, y bounds and update axes data limits.
	flatseglist = [s for seg in allsegs for s in seg]
	points = np.concatenate(flatseglist, axis=0)
	self._mins = points.min(axis=0)
	self._maxs = points.max(axis=0)

	# Each entry in (allsegs, allkinds) is a list of (segs, kinds): segs is a list
	# of (N, 2) arrays of xy coordinates, kinds is a list of arrays of corresponding
	# pathcodes. However, kinds can also be None; in which case all paths in that
	# list are codeless (this case is normalized above). These lists are used to
	# construct paths, which then get concatenated.
	self._paths = [Path.make_compound_path(*map(Path, segs, kinds))
	for segs, kinds in zip(allsegs, allkinds)]

	return kwargs

	def _make_paths_from_contour_generator(self):
	"""Compute ``paths`` using C extension."""
	if self._paths is not None:
	return self._paths
	cg = self._contour_generator
	empty_path = Path(np.empty((0, 2)))
	vertices_and_codes = (
	map(cg.create_filled_contour, *self._get_lowers_and_uppers())
	if self.filled else
	map(cg.create_contour, self.levels))
	return [Path(np.concatenate(vs), np.concatenate(cs)) if len(vs) else empty_path
	for vs, cs in vertices_and_codes]

	def _get_lowers_and_uppers(self):
	"""
	Return ``(lowers, uppers)`` for filled contours.
	"""
	lowers = self._levels[:-1]
	if self.zmin == lowers[0]:
	# Include minimum values in lowest interval
	lowers = lowers.copy() # so we don't change self._levels
	if self.logscale:
	lowers[0] = 0.99 * self.zmin
	else:
	lowers[0] -= 1
	uppers = self._levels[1:]
	return (lowers, uppers)

	def changed(self):
	if not hasattr(self, "cvalues"):
	self._process_colors() # Sets cvalues.
	# Force an autoscale immediately because self.to_rgba() calls
	# autoscale_None() internally with the data passed to it,
	# so if vmin/vmax are not set yet, this would override them with
	# content from cvalues rather than levels like we want
	self.norm.autoscale_None(self.levels)
	self.set_array(self.cvalues)
	self.update_scalarmappable()
	alphas = np.broadcast_to(self.get_alpha(), len(self.cvalues))
	for label, cv, alpha in zip(self.labelTexts, self.labelCValues, alphas):
	label.set_alpha(alpha)
	label.set_color(self.labelMappable.to_rgba(cv))
	super().changed()

	def _autolev(self, N):
	"""
	Select contour levels to span the data.

	The target number of levels, N, is used only when the
	scale is not log and default locator is used.

	We need two more levels for filled contours than for
	line contours, because for the latter we need to specify
	the lower and upper boundary of each range. For example,
	a single contour boundary, say at z = 0, requires only
	one contour line, but two filled regions, and therefore
	three levels to provide boundaries for both regions.
	"""
	if self.locator is None:
	if self.logscale:
	self.locator = ticker.LogLocator()
	else:
	self.locator = ticker.MaxNLocator(N + 1, min_n_ticks=1)

	lev = self.locator.tick_values(self.zmin, self.zmax)

	try:
	if self.locator._symmetric:
	return lev
	except AttributeError:
	pass

	# Trim excess levels the locator may have supplied.
	under = np.nonzero(lev < self.zmin)[0]
	i0 = under[-1] if len(under) else 0
	over = np.nonzero(lev > self.zmax)[0]
	i1 = over[0] + 1 if len(over) else len(lev)
	if self.extend in ('min', 'both'):
	i0 += 1
	if self.extend in ('max', 'both'):
	i1 -= 1

	if i1 - i0 < 3:
	i0, i1 = 0, len(lev)

	return lev[i0:i1]

	def _process_contour_level_args(self, args, z_dtype):
	"""
	Determine the contour levels and store in self.levels.
	"""
	if self.levels is None:
	if args:
	levels_arg = args[0]
	elif np.issubdtype(z_dtype, bool):
	if self.filled:
	levels_arg = [0, .5, 1]
	else:
	levels_arg = [.5]
	else:
	levels_arg = 7 # Default, hard-wired.
	else:
	levels_arg = self.levels
	if isinstance(levels_arg, Integral):
	self.levels = self._autolev(levels_arg)
	else:
	self.levels = np.asarray(levels_arg, np.float64)
	if self.filled and len(self.levels) < 2:
	raise ValueError("Filled contours require at least 2 levels.")
	if len(self.levels) > 1 and np.min(np.diff(self.levels)) <= 0.0:
	raise ValueError("Contour levels must be increasing")

	def _process_levels(self):
	"""
	Assign values to :attr:`layers` based on :attr:`levels`,
	adding extended layers as needed if contours are filled.

	For line contours, layers simply coincide with levels;
	a line is a thin layer. No extended levels are needed
	with line contours.
	"""
	# Make a private _levels to include extended regions; we
	# want to leave the original levels attribute unchanged.
	# (Colorbar needs this even for line contours.)
	self._levels = list(self.levels)

	if self.logscale:
	lower, upper = 1e-250, 1e250
	else:
	lower, upper = -1e250, 1e250

	if self.extend in ('both', 'min'):
	self._levels.insert(0, lower)
	if self.extend in ('both', 'max'):
	self._levels.append(upper)
	self._levels = np.asarray(self._levels)

	if not self.filled:
	self.layers = self.levels
	return

	# Layer values are mid-way between levels in screen space.
	if self.logscale:
	# Avoid overflow by taking sqrt before multiplying.
	self.layers = (np.sqrt(self._levels[:-1])
	* np.sqrt(self._levels[1:]))
	else:
	self.layers = 0.5 * (self._levels[:-1] + self._levels[1:])

	def _process_colors(self):
	"""
	Color argument processing for contouring.

	Note that we base the colormapping on the contour levels
	and layers, not on the actual range of the Z values. This
	means we don't have to worry about bad values in Z, and we
	always have the full dynamic range available for the selected
	levels.

	The color is based on the midpoint of the layer, except for
	extended end layers. By default, the norm vmin and vmax
	are the extreme values of the non-extended levels. Hence,
	the layer color extremes are not the extreme values of
	the colormap itself, but approach those values as the number
	of levels increases. An advantage of this scheme is that
	line contours, when added to filled contours, take on
	colors that are consistent with those of the filled regions;
	for example, a contour line on the boundary between two
	regions will have a color intermediate between those
	of the regions.

	"""
	self.monochrome = self.cmap.monochrome
	if self.colors is not None:
	# Generate integers for direct indexing.
	i0, i1 = 0, len(self.levels)
	if self.filled:
	i1 -= 1
	# Out of range indices for over and under:
	if self.extend in ('both', 'min'):
	i0 -= 1
	if self.extend in ('both', 'max'):
	i1 += 1
	self.cvalues = list(range(i0, i1))
	self.set_norm(mcolors.NoNorm())
	else:
	self.cvalues = self.layers
	self.norm.autoscale_None(self.levels)
	self.set_array(self.cvalues)
	self.update_scalarmappable()
	if self.extend in ('both', 'max', 'min'):
	self.norm.clip = False

	def _process_linewidths(self, linewidths):
	Nlev = len(self.levels)
	if linewidths is None:
	default_linewidth = mpl.rcParams['contour.linewidth']
	if default_linewidth is None:
	default_linewidth = mpl.rcParams['lines.linewidth']
	return [default_linewidth] * Nlev
	elif not np.iterable(linewidths):
	return [linewidths] * Nlev
	else:
	linewidths = list(linewidths)
	return (linewidths * math.ceil(Nlev / len(linewidths)))[:Nlev]

	def _process_linestyles(self, linestyles):
	Nlev = len(self.levels)
	if linestyles is None:
	tlinestyles = ['solid'] * Nlev
	if self.monochrome:
	eps = - (self.zmax - self.zmin) * 1e-15
	for i, lev in enumerate(self.levels):
	if lev < eps:
	tlinestyles[i] = self.negative_linestyles
	else:
	if isinstance(linestyles, str):
	tlinestyles = [linestyles] * Nlev
	elif np.iterable(linestyles):
	tlinestyles = list(linestyles)
	if len(tlinestyles) < Nlev:
	nreps = int(np.ceil(Nlev / len(linestyles)))
	tlinestyles = tlinestyles * nreps
	if len(tlinestyles) > Nlev:
	tlinestyles = tlinestyles[:Nlev]
	else:
	raise ValueError("Unrecognized type for linestyles kwarg")
	return tlinestyles

	def _find_nearest_contour(self, xy, indices=None):
	"""
	Find the point in the unfilled contour plot that is closest (in screen
	space) to point xy.

	Parameters
	----------
	xy : tuple[float, float]
	The reference point (in screen space).
	indices : list of int or None, default: None
	Indices of contour levels to consider. If None (the default), all levels
	are considered.

	Returns
	-------
	idx_level_min : int
	The index of the contour level closest to xy.
	idx_vtx_min : int
	The index of the `.Path` segment closest to xy (at that level).
	proj : (float, float)
	The point in the contour plot closest to xy.
	"""

	# Convert each contour segment to pixel coordinates and then compare the given
	# point to those coordinates for each contour. This is fast enough in normal
	# cases, but speedups may be possible.

	if self.filled:
	raise ValueError("Method does not support filled contours")

	if indices is None:
	indices = range(len(self._paths))

	d2min = np.inf
	idx_level_min = idx_vtx_min = proj_min = None

	for idx_level in indices:
	path = self._paths[idx_level]
	idx_vtx_start = 0
	for subpath in path._iter_connected_components():
	if not len(subpath.vertices):
	continue
	lc = self.get_transform().transform(subpath.vertices)
	d2, proj, leg = _find_closest_point_on_path(lc, xy)
	if d2 < d2min:
	d2min = d2
	idx_level_min = idx_level
	idx_vtx_min = leg[1] + idx_vtx_start
	proj_min = proj
	idx_vtx_start += len(subpath)

	return idx_level_min, idx_vtx_min, proj_min

	def find_nearest_contour(self, x, y, indices=None, pixel=True):
	"""
	Find the point in the contour plot that is closest to ``(x, y)``.

	This method does not support filled contours.

	Parameters
	----------
	x, y : float
	The reference point.
	indices : list of int or None, default: None
	Indices of contour levels to consider. If None (the default), all
	levels are considered.
	pixel : bool, default: True
	If True, measure distance in pixel (screen) space, which is
	useful for manual contour labeling; else, measure distance in axes
	space.

	Returns
	-------
	path : int
	The index of the path that is closest to ``(x, y)``. Each path corresponds
	to one contour level.
	subpath : int
	The index within that closest path of the subpath that is closest to
	``(x, y)``. Each subpath corresponds to one unbroken contour line.
	index : int
	The index of the vertices within that subpath that are closest to
	``(x, y)``.
	xmin, ymin : float
	The point in the contour plot that is closest to ``(x, y)``.
	d2 : float
	The squared distance from ``(xmin, ymin)`` to ``(x, y)``.
	"""
	segment = index = d2 = None

	with ExitStack() as stack:
	if not pixel:
	# _find_nearest_contour works in pixel space. We want axes space, so
	# effectively disable the transformation here by setting to identity.
	stack.enter_context(self._cm_set(
	transform=mtransforms.IdentityTransform()))

	i_level, i_vtx, (xmin, ymin) = self._find_nearest_contour((x, y), indices)

	if i_level is not None:
	cc_cumlens = np.cumsum(
	[*map(len, self._paths[i_level]._iter_connected_components())])
	segment = cc_cumlens.searchsorted(i_vtx, "right")
	index = i_vtx if segment == 0 else i_vtx - cc_cumlens[segment - 1]
	d2 = (xmin-x)2 + (ymin-y)2

	return (i_level, segment, index, xmin, ymin, d2)

	def draw(self, renderer):
	paths = self._paths
	n_paths = len(paths)
	if not self.filled or all(hatch is None for hatch in self.hatches):
	super().draw(renderer)
	return
	# In presence of hatching, draw contours one at a time.
	edgecolors = self.get_edgecolors()
	if edgecolors.size == 0:
	edgecolors = ("none",)
	for idx in range(n_paths):
	with cbook._setattr_cm(self, _paths=[paths[idx]]), self._cm_set(
	hatch=self.hatches[idx % len(self.hatches)],
	array=[self.get_array()[idx]],
	linewidths=[self.get_linewidths()[idx % len(self.get_linewidths())]],
	linestyles=[self.get_linestyles()[idx % len(self.get_linestyles())]],
	edgecolors=edgecolors[idx % len(edgecolors)],
	):
	super().draw(renderer)


	@_docstring.interpd
	class QuadContourSet(ContourSet):
	"""
	Create and store a set of contour lines or filled regions.

	This class is typically not instantiated directly by the user but by
	`~.Axes.contour` and `~.Axes.contourf`.

	%(contour_set_attributes)s
	"""

	def _process_args(self, args, corner_mask=None, algorithm=None, *kwargs):
	"""
	Process args and kwargs.
	"""
	if args and isinstance(args[0], QuadContourSet):
	if self.levels is None:
	self.levels = args[0].levels
	self.zmin = args[0].zmin
	self.zmax = args[0].zmax
	self._corner_mask = args[0]._corner_mask
	contour_generator = args[0]._contour_generator
	self._mins = args[0]._mins
	self._maxs = args[0]._maxs
	self._algorithm = args[0]._algorithm
	else:
	import contourpy

	if algorithm is None:
	algorithm = mpl.rcParams['contour.algorithm']
	mpl.rcParams.validate["contour.algorithm"](algorithm)
	self._algorithm = algorithm

	if corner_mask is None:
	if self._algorithm == "mpl2005":
	# mpl2005 does not support corner_mask=True so if not
	# specifically requested then disable it.
	corner_mask = False
	else:
	corner_mask = mpl.rcParams['contour.corner_mask']
	self._corner_mask = corner_mask

	x, y, z = self._contour_args(args, kwargs)

	contour_generator = contourpy.contour_generator(
	x, y, z, name=self._algorithm, corner_mask=self._corner_mask,
	line_type=contourpy.LineType.SeparateCode,
	fill_type=contourpy.FillType.OuterCode,
	chunk_size=self.nchunk)

	t = self.get_transform()

	# if the transform is not trans data, and some part of it
	# contains transData, transform the xs and ys to data coordinates
	if (t != self.axes.transData and
	any(t.contains_branch_seperately(self.axes.transData))):
	trans_to_data = t - self.axes.transData
	pts = np.vstack([x.flat, y.flat]).T
	transformed_pts = trans_to_data.transform(pts)
	x = transformed_pts[..., 0]
	y = transformed_pts[..., 1]

	self._mins = [ma.min(x), ma.min(y)]
	self._maxs = [ma.max(x), ma.max(y)]

	self._contour_generator = contour_generator

	return kwargs

	def _contour_args(self, args, kwargs):
	if self.filled:
	fn = 'contourf'
	else:
	fn = 'contour'
	nargs = len(args)

	if 0 < nargs <= 2:
	z, *args = args
	z = ma.asarray(z)
	x, y = self._initialize_x_y(z)
	elif 2 < nargs <= 4:
	x, y, z_orig, *args = args
	x, y, z = self._check_xyz(x, y, z_orig, kwargs)

	else:
	raise _api.nargs_error(fn, takes="from 1 to 4", given=nargs)
	z = ma.masked_invalid(z, copy=False)
	self.zmax = z.max().astype(float)
	self.zmin = z.min().astype(float)
	if self.logscale and self.zmin <= 0:
	z = ma.masked_where(z <= 0, z)
	_api.warn_external('Log scale: values of z <= 0 have been masked')
	self.zmin = z.min().astype(float)
	self._process_contour_level_args(args, z.dtype)
	return (x, y, z)

	def _check_xyz(self, x, y, z, kwargs):
	"""
	Check that the shapes of the input arrays match; if x and y are 1D,
	convert them to 2D using meshgrid.
	"""
	x, y = self.axes._process_unit_info([("x", x), ("y", y)], kwargs)

	x = np.asarray(x, dtype=np.float64)
	y = np.asarray(y, dtype=np.float64)
	z = ma.asarray(z)

	if z.ndim != 2:
	raise TypeError(f"Input z must be 2D, not {z.ndim}D")
	if z.shape[0] < 2 or z.shape[1] < 2:
	raise TypeError(f"Input z must be at least a (2, 2) shaped array, "
	f"but has shape {z.shape}")
	Ny, Nx = z.shape

	if x.ndim != y.ndim:
	raise TypeError(f"Number of dimensions of x ({x.ndim}) and y "
	f"({y.ndim}) do not match")
	if x.ndim == 1:
	nx, = x.shape
	ny, = y.shape
	if nx != Nx:
	raise TypeError(f"Length of x ({nx}) must match number of "
	f"columns in z ({Nx})")
	if ny != Ny:
	raise TypeError(f"Length of y ({ny}) must match number of "
	f"rows in z ({Ny})")
	x, y = np.meshgrid(x, y)
	elif x.ndim == 2:
	if x.shape != z.shape:
	raise TypeError(
	f"Shapes of x {x.shape} and z {z.shape} do not match")
	if y.shape != z.shape:
	raise TypeError(
	f"Shapes of y {y.shape} and z {z.shape} do not match")
	else:
	raise TypeError(f"Inputs x and y must be 1D or 2D, not {x.ndim}D")

	return x, y, z

	def _initialize_x_y(self, z):
	"""
	Return X, Y arrays such that contour(Z) will match imshow(Z)
	if origin is not None.
	The center of pixel Z[i, j] depends on origin:
	if origin is None, x = j, y = i;
	if origin is 'lower', x = j + 0.5, y = i + 0.5;
	if origin is 'upper', x = j + 0.5, y = Nrows - i - 0.5
	If extent is not None, x and y will be scaled to match,
	as in imshow.
	If origin is None and extent is not None, then extent
	will give the minimum and maximum values of x and y.
	"""
	if z.ndim != 2:
	raise TypeError(f"Input z must be 2D, not {z.ndim}D")
	elif z.shape[0] < 2 or z.shape[1] < 2:
	raise TypeError(f"Input z must be at least a (2, 2) shaped array, "
	f"but has shape {z.shape}")
	else:
	Ny, Nx = z.shape
	if self.origin is None: # Not for image-matching.
	if self.extent is None:
	return np.meshgrid(np.arange(Nx), np.arange(Ny))
	else:
	x0, x1, y0, y1 = self.extent
	x = np.linspace(x0, x1, Nx)
	y = np.linspace(y0, y1, Ny)
	return np.meshgrid(x, y)
	# Match image behavior:
	if self.extent is None:
	x0, x1, y0, y1 = (0, Nx, 0, Ny)
	else:
	x0, x1, y0, y1 = self.extent
	dx = (x1 - x0) / Nx
	dy = (y1 - y0) / Ny
	x = x0 + (np.arange(Nx) + 0.5) * dx
	y = y0 + (np.arange(Ny) + 0.5) * dy
	if self.origin == 'upper':
	y = y[::-1]
	return np.meshgrid(x, y)


	_docstring.interpd.register(contour_doc="""
	`.contour` and `.contourf` draw contour lines and filled contours,
	respectively. Except as noted, function signatures and return values
	are the same for both versions.

	Parameters
	----------
	X, Y : array-like, optional
	The coordinates of the values in Z.

	X and Y must both be 2D with the same shape as Z (e.g.
	created via `numpy.meshgrid`), or they must both be 1-D such
	that ``len(X) == N`` is the number of columns in Z and
	``len(Y) == M`` is the number of rows in Z.

	X and Y must both be ordered monotonically.

	If not given, they are assumed to be integer indices, i.e.
	``X = range(N)``, ``Y = range(M)``.

	Z : (M, N) array-like
	The height values over which the contour is drawn. Color-mapping is
	controlled by cmap, norm, vmin, and vmax.

	levels : int or array-like, optional
	Determines the number and positions of the contour lines / regions.

	If an int n, use `~matplotlib.ticker.MaxNLocator`, which tries
	to automatically choose no more than n+1 "nice" contour levels
	between minimum and maximum numeric values of Z.

	If array-like, draw contour lines at the specified levels.
	The values must be in increasing order.

	Returns
	-------
	`~.contour.QuadContourSet`

	Other Parameters
	----------------
	corner_mask : bool, default: :rc:`contour.corner_mask`
	Enable/disable corner masking, which only has an effect if Z is
	a masked array. If ``False``, any quad touching a masked point is
	masked out. If ``True``, only the triangular corners of quads
	nearest those points are always masked out, other triangular
	corners comprising three unmasked points are contoured as usual.

	colors : :mpltype:`color` or list of :mpltype:`color`, optional
	The colors of the levels, i.e. the lines for `.contour` and the
	areas for `.contourf`.

	The sequence is cycled for the levels in ascending order. If the
	sequence is shorter than the number of levels, it's repeated.

	As a shortcut, a single color may be used in place of one-element lists, i.e.
	``'red'`` instead of ``['red']`` to color all levels with the same color.

	.. versionchanged:: 3.10
	Previously a single color had to be expressed as a string, but now any
	valid color format may be passed.

	By default (value None), the colormap specified by cmap
	will be used.

	alpha : float, default: 1
	The alpha blending value, between 0 (transparent) and 1 (opaque).

	%(cmap_doc)s

	This parameter is ignored if colors is set.

	%(norm_doc)s

	This parameter is ignored if colors is set.

	%(vmin_vmax_doc)s

	If vmin or vmax are not given, the default color scaling is based on
	levels.

	This parameter is ignored if colors is set.

	%(colorizer_doc)s

	This parameter is ignored if colors is set.

	origin : {None, 'upper', 'lower', 'image'}, default: None
	Determines the orientation and exact position of Z by specifying
	the position of ``Z[0, 0]``. This is only relevant, if X, Y
	are not given.

	- None: ``Z[0, 0]`` is at X=0, Y=0 in the lower left corner.
	- 'lower': ``Z[0, 0]`` is at X=0.5, Y=0.5 in the lower left corner.
	- 'upper': ``Z[0, 0]`` is at X=N+0.5, Y=0.5 in the upper left
	corner.
	- 'image': Use the value from :rc:`image.origin`.

	extent : (x0, x1, y0, y1), optional
	If origin is not None, then extent is interpreted as in
	`.imshow`: it gives the outer pixel boundaries. In this case, the
	position of Z[0, 0] is the center of the pixel, not a corner. If
	origin is None, then (x0, y0) is the position of Z[0, 0],
	and (x1, y1) is the position of Z[-1, -1].

	This argument is ignored if X and Y are specified in the call
	to contour.

	locator : ticker.Locator subclass, optional
	The locator is used to determine the contour levels if they
	are not given explicitly via levels.
	Defaults to `~.ticker.MaxNLocator`.

	extend : {'neither', 'both', 'min', 'max'}, default: 'neither'
	Determines the ``contourf``-coloring of values that are outside the
	levels range.

	If 'neither', values outside the levels range are not colored.
	If 'min', 'max' or 'both', color the values below, above or below
	and above the levels range.

	Values below ``min(levels)`` and above ``max(levels)`` are mapped
	to the under/over values of the `.Colormap`. Note that most
	colormaps do not have dedicated colors for these by default, so
	that the over and under values are the edge values of the colormap.
	You may want to set these values explicitly using
	`.Colormap.set_under` and `.Colormap.set_over`.

	.. note::

	An existing `.QuadContourSet` does not get notified if
	properties of its colormap are changed. Therefore, an explicit
	call `~.ContourSet.changed()` is needed after modifying the
	colormap. The explicit call can be left out, if a colorbar is
	assigned to the `.QuadContourSet` because it internally calls
	`~.ContourSet.changed()`.

	Example::

	x = np.arange(1, 10)
	y = x.reshape(-1, 1)
	h = x * y

	cs = plt.contourf(h, levels=[10, 30, 50],
	colors=['#808080', '#A0A0A0', '#C0C0C0'], extend='both')
	cs.cmap.set_over('red')
	cs.cmap.set_under('blue')
	cs.changed()

	xunits, yunits : registered units, optional
	Override axis units by specifying an instance of a
	:class:`matplotlib.units.ConversionInterface`.

	antialiased : bool, optional
	Enable antialiasing, overriding the defaults. For
	filled contours, the default is False. For line contours,
	it is taken from :rc:`lines.antialiased`.

	nchunk : int >= 0, optional
	If 0, no subdivision of the domain. Specify a positive integer to
	divide the domain into subdomains of nchunk by nchunk quads.
	Chunking reduces the maximum length of polygons generated by the
	contouring algorithm which reduces the rendering workload passed
	on to the backend and also requires slightly less RAM. It can
	however introduce rendering artifacts at chunk boundaries depending
	on the backend, the antialiased flag and value of alpha.

	linewidths : float or array-like, default: :rc:`contour.linewidth`
	Only applies to `.contour`.

	The line width of the contour lines.

	If a number, all levels will be plotted with this linewidth.

	If a sequence, the levels in ascending order will be plotted with
	the linewidths in the order specified.

	If None, this falls back to :rc:`lines.linewidth`.

	linestyles : {None, 'solid', 'dashed', 'dashdot', 'dotted'}, optional
	Only applies to `.contour`.

	If linestyles is None, the default is 'solid' unless the lines are
	monochrome. In that case, negative contours will instead take their
	linestyle from the negative_linestyles argument.

	linestyles can also be an iterable of the above strings specifying a set
	of linestyles to be used. If this iterable is shorter than the number of
	contour levels it will be repeated as necessary.

	negative_linestyles : {None, 'solid', 'dashed', 'dashdot', 'dotted'}, \
	optional
	Only applies to `.contour`.

	If linestyles is None and the lines are monochrome, this argument
	specifies the line style for negative contours.

	If negative_linestyles is None, the default is taken from
	:rc:`contour.negative_linestyle`.

	negative_linestyles can also be an iterable of the above strings
	specifying a set of linestyles to be used. If this iterable is shorter than
	the number of contour levels it will be repeated as necessary.

	hatches : list[str], optional
	Only applies to `.contourf`.

	A list of cross hatch patterns to use on the filled areas.
	If None, no hatching will be added to the contour.

	algorithm : {'mpl2005', 'mpl2014', 'serial', 'threaded'}, optional
	Which contouring algorithm to use to calculate the contour lines and
	polygons. The algorithms are implemented in
	`ContourPy <https://github.com/contourpy/contourpy>`_, consult the
	`ContourPy documentation <https://contourpy.readthedocs.io>`_ for
	further information.

	The default is taken from :rc:`contour.algorithm`.

	clip_path : `~matplotlib.patches.Patch` or `.Path` or `.TransformedPath`
	Set the clip path. See `~matplotlib.artist.Artist.set_clip_path`.

	.. versionadded:: 3.8

	data : indexable object, optional
	DATA_PARAMETER_PLACEHOLDER

	Notes
	-----
	1. `.contourf` differs from the MATLAB version in that it does not draw
	the polygon edges. To draw edges, add line contours with calls to
	`.contour`.

	2. `.contourf` fills intervals that are closed at the top; that is, for
	boundaries z1 and z2, the filled region is::

	z1 < Z <= z2

	except for the lowest interval, which is closed on both sides (i.e.
	it includes the lowest value).

	3. `.contour` and `.contourf` use a `marching squares
	<https://en.wikipedia.org/wiki/Marching_squares>`_ algorithm to
	compute contour locations. More information can be found in
	`ContourPy documentation <https://contourpy.readthedocs.io>`_.
	""" % _docstring.interpd.params)

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